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MiniRAG: Towards Extremely Simple Retrieval-Augmented Generation

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arxiv 2501.06713 v3 pith:S6EXA646 submitted 2025-01-12 cs.AI

Tianyu Fan , Jingyuan Wang , Xubin Ren , Chao Huang This is my paper
classification cs.AI
keywords miniraglightweightslmscapabilitiescomplexefficientgenerationgraph
verification ladder T0 review T1 audit T2 compute T3 formal
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The growing demand for efficient and lightweight Retrieval-Augmented Generation (RAG) systems has highlighted significant challenges when deploying Small Language Models (SLMs) in existing RAG frameworks. Current approaches face severe performance degradation due to SLMs' limited semantic understanding and text processing capabilities, creating barriers for widespread adoption in resource-constrained scenarios. To address these fundamental limitations, we present MiniRAG, a novel RAG system designed for extreme simplicity and efficiency. MiniRAG introduces two key technical innovations: (1) a semantic-aware heterogeneous graph indexing mechanism that combines text chunks and named entities in a unified structure, reducing reliance on complex semantic understanding, and (2) a lightweight topology-enhanced retrieval approach that leverages graph structures for efficient knowledge discovery without requiring advanced language capabilities. Our extensive experiments demonstrate that MiniRAG achieves comparable performance to LLM-based methods even when using SLMs while requiring only 25\% of the storage space. Additionally, we contribute a comprehensive benchmark dataset for evaluating lightweight RAG systems under realistic on-device scenarios with complex queries. We fully open-source our implementation and datasets at: https://github.com/HKUDS/MiniRAG.

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Cited by 5 Pith papers

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  1. Agents-K1: Towards Agent-native Knowledge Orchestration

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    Agents-K1 is an end-to-end pipeline with a multimodal parser, 4B GRPO-trained extractor, and agent CLI that builds scientific knowledge graphs from full papers and was run on 2.46 million documents to produce Scholar-KG.

  2. NGM-RAG: Neural Graph Matching based Retrieval-Augmented Generation

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    Combining Levenshtein, BM25, and GNN node matching with adaptive weights yields higher EM/F1 and win rates than NaiveRAG, GraphRAG, and LightRAG on multi-hop QA and long-context tasks.

  3. GroupRAG: Cognitively Inspired Group-Aware Retrieval and Reasoning via Knowledge-Driven Problem Structuring

    cs.IR 2026-03 conditional novelty 5.0 of 10

    Structuring questions into knowledge-driven keypoint groups before retrieval and reasoning improves small-model accuracy on MedQA.

  4. SafeDriveRAG: Towards Safe Autonomous Driving with Knowledge Graph-based Retrieval-Augmented Generation

    cs.AI 2025-07 conditional novelty 5.0 of 10

    SafeDrive228K is a 228K-example multimodal QA benchmark for traffic safety, and a graph-based RAG method improves VLM accuracy on it by 4.7 to 14.6 points across five models.

  5. MobileRAG: A Fast, Memory-Efficient, and Energy-Efficient Method for On-Device RAG

    cs.DB 2025-07 conditional novelty 4.0 of 10

    A fully on-device RAG pipeline using a partitioned, partially disk-loaded graph index and selective sentence-window reduction claims 1.72-8.89x faster vector search and up to 40.2% lower power than baselines, with com...

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