MiniRAG: Towards Extremely Simple Retrieval-Augmented Generation (2501.06713v3)

Abstract: The growing demand for efficient and lightweight Retrieval-Augmented Generation (RAG) systems has highlighted significant challenges when deploying Small LLMs (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.

• The paper introduces MiniRAG, a framework that leverages structural knowledge and graph-based retrieval to enable efficient retrieval-augmented generation with small language models.
• It demonstrates performance parity with LLM-based systems by achieving 1.3-2.5 times effectiveness and reducing storage needs to only 25%, with an accuracy drop of just 0.8%-20%.
• The work underlines practical benefits for on-device processing and promotes further research into lightweight AI systems using efficient, graph-based retrieval methods.

MiniRAG: Towards Extremely Simple Retrieval-Augmented Generation

In the examined paper, the authors introduce MiniRAG, a streamlined framework for Retrieval-Augmented Generation (RAG) designed to function effectively with Small LLMs (SLMs). The motivation lies in the computational inefficiency and resource-intensiveness of traditional RAG systems that rely extensively on LLMs, making them unfeasible for resource-constrained environments like edge devices. The paper outlines technical innovations within MiniRAG that aim to leverage SLMs without compromising performance by focusing on structural knowledge representation and efficient graph-based retrieval mechanisms.

Key Innovations and Architecture

MiniRAG addresses the limitations of SLMs through two primary innovations:

1. Semantic-aware Heterogeneous Graph Indexing: This mechanism integrates both text chunks and named entities into a unified structure. By systematically combining these elements, the system reduces dependency on complex semantic understanding, which is typically a limitation for SLMs. This model captures key relationships and provides a detailed context through the graph's interconnected nodes and edges.
2. Lightweight Topology-Enhanced Retrieval: Utilizing graph structures, MiniRAG performs efficient knowledge discovery by leveraging heuristic search patterns. The graph architecture allows for semantic retrieval without necessitating advanced language comprehension, thus aligning with SLM capabilities.

These components are synergistically configured to maintain robust RAG performance even with lightweight models. This is accomplished by emphasizing explicit structural cues over intricate semantic details, a departure from traditional LLM-centric designs that require substantial computational resources.

Experimental Evaluation

The researchers conducted extensive experiments across multiple datasets, comparing MiniRAG against existing RAG solutions. MiniRAG demonstrated performance parity with LLM-based systems, achieving 1.3-2.5 times effectiveness relative to existing lightweight RAG systems, while reducing storage requirements to just 25% of those needed by traditional methods when using LLMs. Notably, the transition from LLMs to SLMs preserved system robustness, with an accuracy drop ranging merely from 0.8% to 20%.

Component Efficacy and System Performance

A detailed component analysis was carried out to evaluate the contributions of MiniRAG's core components. By removing edge information or chunk nodes, researchers witnessed significant performance degradation, underscoring the importance of these elements in query-guided reasoning path discovery. This highlights the system’s reliance on structural representations for compensating semantic understanding limitations of SLMs.

Practical and Theoretical Implications

The paper suggests significant practical implications, especially for applications requiring on-device processing where data privacy or computational resources are a concern. By reducing the dependency on complex semantic capabilities, MiniRAG enables efficient and practical deployment of RAG systems in environments where LLMs are not viable.

From a theoretical perspective, MiniRAG opens avenues for future research in lightweight AI systems, challenging the normative dependence on LLMs by showcasing efficient alternatives. This potentially stimulates further development in structural knowledge representation and the integration of graph-based retrieval into versatile AI applications.

Conclusion and Future Directions

MiniRAG signifies a shift towards more computationally efficient RAG systems that do not rely heavily on powerful LLMs. The approach offers a viable solution for resource-constrained applications while delivering robust retrieval and generation capabilities. Future research may delve into optimizing SLMs in other AI domains or further refining graph-based approaches to expand the capabilities and applicability of lightweight LLM systems.