mlsanityData to 14 Oct 2026

UltraRAG: A Modular and Automated Toolkit for Adaptive Retrieval-Augmented Generation

arXiv 2504.08761 ↗First posted 31 Mar 2025cs.IRcited by 6

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

Abstract (arXiv)

Retrieval-Augmented Generation (RAG) significantly enhances the performance of large language models (LLMs) in downstream tasks by integrating external knowledge. To facilitate researchers in deploying RAG systems, various RAG toolkits have been introduced. However, many existing RAG toolkits lack support for knowledge adaptation tailored to specific application scenarios. To address this limitation, we propose UltraRAG, a RAG toolkit that automates knowledge adaptation throughout the entire workflow, from data construction and training to evaluation, while ensuring ease of use. UltraRAG features a user-friendly WebUI that streamlines the RAG process, allowing users to build and optimize systems without coding expertise. It supports multimodal input and provides comprehensive tools for managing the knowledge base. With its highly modular architecture, UltraRAG delivers an end-to-end development solution, enabling seamless knowledge adaptation across diverse user scenarios. The code, demonstration videos, and installable package for UltraRAG are publicly available at https://github.com/OpenBMB/UltraRAG.

Key facts. Cited by 6 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 26 of its 31 references that are in the map. First posted on arXiv 31 Mar 2025.

Builds on

26 of 31 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework2 Aug
  2. 2024FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research22 May

Other references · 24

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  5. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 8,065
  6. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  7. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  8. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  9. 2024MiniCPM-V: A GPT-4V Level MLLM on Your Phonecited by 1,079
  10. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
  11. 2020Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrievalcited by 849
  12. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 792
12 more
  1. 2023In-Context Retrieval-Augmented Language Modelscited by 702
  2. 2023RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentationcited by 256
  3. 2024VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documentscited by 245
  4. 2023ChatDB: Augmenting LLMs with Databases as Their Symbolic Memorycited by 140
  5. 2021Efficient Nearest Neighbor Language Modelscited by 93
  6. 2023Augmentation-Adapted Retriever Improves Generalization of Language Models as Generic Plug-Incited by 71
  7. 2018Skeleton-to-Response: Dialogue Generation Guided by Retrieval Memorycited by 49
  8. 2024RAG-DDR: Optimizing Retrieval-Augmented Generation Using Differentiable Data Rewardscited by 33
  9. 2023MARVEL: Unlocking the Multi-Modal Capability of Dense Retrieval via Visual Module Plugincited by 22
  10. 2024RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generationcited by 8
  11. 2024LocalRQA: From Generating Data to Locally Training, Testing, and Deploying Retrieval-Augmented QA Systemscited by 7
  12. 2024KBAlign: Efficient Self Adaptation on Specific Knowledge Basescited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 4.

6 papers.

  1. 2026MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokenscited by 6
  2. 2025Scaling External Knowledge Input Beyond Context Windows of LLMs via Multi-Agent Collaborationcited by 4
  3. 2026Know More, Know Clearer: A Meta-Cognitive Framework for Knowledge Augmentation in Large Language Modelscited by 3
  4. 2026SEEK: Steering LLM Reasoning for RAG via Internal Reasoning Sketchescited by 2
  5. 2025ClueAnchor: Clue-Anchored Knowledge Reasoning Exploration and Optimization for Retrieval-Augmented Generationcited by 1
  6. 2026AgentCPM-Report: Interleaving Drafting and Deepening for Open-Ended Deep Researchcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025Dynamic and Parametric Retrieval-Augmented Generation
  2. 2024XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation
  3. 2024AutoRAG: Automated Framework for optimization of Retrieval Augmented Generation Pipeline
  4. 2024RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation
  5. 2025FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation
  6. 2023Retrieval-Augmented Generation for Large Language Models: A Survey
  7. 2025Dynamic Parametric Retrieval Augmented Generation for Test-time Knowledge Enhancement
  8. 2025mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs