LightRAG: Simple and Fast Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2410.05779v2
• Date: Thu, 7 Nov 2024 10:44:59 GMT
• Title: LightRAG: Simple and Fast Retrieval-Augmented Generation
• Authors: Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, Chao Huang,
• Abstract summary: Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources. Existing RAG systems have significant limitations, including reliance on flat data representations and inadequate contextual awareness. We propose LightRAG, which incorporates graph structures into text indexing and retrieval processes.
• Score: 12.86888202297654
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tailored to user needs. However, existing RAG systems have significant limitations, including reliance on flat data representations and inadequate contextual awareness, which can lead to fragmented answers that fail to capture complex inter-dependencies. To address these challenges, we propose LightRAG, which incorporates graph structures into text indexing and retrieval processes. This innovative framework employs a dual-level retrieval system that enhances comprehensive information retrieval from both low-level and high-level knowledge discovery. Additionally, the integration of graph structures with vector representations facilitates efficient retrieval of related entities and their relationships, significantly improving response times while maintaining contextual relevance. This capability is further enhanced by an incremental update algorithm that ensures the timely integration of new data, allowing the system to remain effective and responsive in rapidly changing data environments. Extensive experimental validation demonstrates considerable improvements in retrieval accuracy and efficiency compared to existing approaches. We have made our LightRAG open-source and available at the link: https://github.com/HKUDS/LightRAG.

Related papers

• Simple is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation [9.844598565914055]
  Large Language Models (LLMs) demonstrate strong reasoning abilities but face limitations such as hallucinations and outdated knowledge. We introduce SubgraphRAG, extending the Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) framework that retrieves subgraphs. Our approach innovatively integrates a lightweight multilayer perceptron with a parallel triple-scoring mechanism for efficient and flexible subgraph retrieval.
  arXiv  Detail & Related papers  (2024-10-28T04:39:32Z)
• An Adaptive Framework for Generating Systematic Explanatory Answer in Online Q&A Platforms [62.878616839799776]
  We propose SynthRAG, an innovative framework designed to enhance Question Answering (QA) performance. SynthRAG improves on conventional models by employing adaptive outlines for dynamic content structuring. An online deployment on the Zhihu platform revealed that SynthRAG's answers achieved notable user engagement.
  arXiv  Detail & Related papers  (2024-10-23T09:14:57Z)
• Retriever-and-Memory: Towards Adaptive Note-Enhanced Retrieval-Augmented Generation [72.70046559930555]
  We propose a generic RAG approach called Adaptive Note-Enhanced RAG (Adaptive-Note) for complex QA tasks. Specifically, Adaptive-Note introduces an overarching view of knowledge growth, iteratively gathering new information in the form of notes. In addition, we employ an adaptive, note-based stop-exploration strategy to decide "what to retrieve and when to stop" to encourage sufficient knowledge exploration.
  arXiv  Detail & Related papers  (2024-10-11T14:03:29Z)
• VERA: Validation and Enhancement for Retrieval Augmented systems [0.0]
  We propose textbfVERA (textbfValidation and textbfEnhancement for textbfRetrieval textbfAugmented systems), a system designed to evaluate and enhance the retrieved context before response generation. VERA employs an evaluator-cum-enhancer LLM that first checks if external retrieval is necessary, evaluates the relevance and redundancy of the retrieved context, and refines it to eliminate non-essential information.
  arXiv  Detail & Related papers  (2024-09-18T16:10:47Z)
• MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery [24.38640001674072]
  Retrieval-Augmented Generation (RAG) leverages retrieval tools to access external databases. Existing RAG systems are primarily effective for straightforward question-answering tasks. We propose MemoRAG, a novel retrieval-augmented generation paradigm empowered by long-term memory.
  arXiv  Detail & Related papers  (2024-09-09T13:20:31Z)
• WeKnow-RAG: An Adaptive Approach for Retrieval-Augmented Generation Integrating Web Search and Knowledge Graphs [10.380692079063467]
  We propose WeKnow-RAG, which integrates Web search and Knowledge Graphs into a "Retrieval-Augmented Generation (RAG)" system. First, the accuracy and reliability of LLM responses are improved by combining the structured representation of Knowledge Graphs with the flexibility of dense vector retrieval. Our approach effectively balances the efficiency and accuracy of information retrieval, thus improving the overall retrieval process.
  arXiv  Detail & Related papers  (2024-08-14T15:19:16Z)
• Large Language Models for Information Retrieval: A Survey [58.30439850203101]
  Information retrieval has evolved from term-based methods to its integration with advanced neural models. Recent research has sought to leverage large language models (LLMs) to improve IR systems. We delve into the confluence of LLMs and IR systems, including crucial aspects such as query rewriters, retrievers, rerankers, and readers.
  arXiv  Detail & Related papers  (2023-08-14T12:47:22Z)
• Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy [164.83371924650294]
  We show that strong performance can be achieved by a method we call Iter-RetGen, which synergizes retrieval and generation in an iterative manner. A model output shows what might be needed to finish a task, and thus provides an informative context for retrieving more relevant knowledge. Iter-RetGen processes all retrieved knowledge as a whole and largely preserves the flexibility in generation without structural constraints.
  arXiv  Detail & Related papers  (2023-05-24T16:17:36Z)
• Synergistic Interplay between Search and Large Language Models for Information Retrieval [141.18083677333848]
  InteR allows RMs to expand knowledge in queries using LLM-generated knowledge collections. InteR achieves overall superior zero-shot retrieval performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-12T11:58:15Z)
• Plug-and-Play Regulators for Image-Text Matching [76.28522712930668]
  Exploiting fine-grained correspondence and visual-semantic alignments has shown great potential in image-text matching. We develop two simple but quite effective regulators which efficiently encode the message output to automatically contextualize and aggregate cross-modal representations. Experiments on MSCOCO and Flickr30K datasets validate that they can bring an impressive and consistent R@1 gain on multiple models.
  arXiv  Detail & Related papers  (2023-03-23T15:42:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.