LLMs之RAG：《LightRAG: Simple and Fast Retrieval-Augmented Generation》翻译与解读

地址

论文地址：https://arxiv.org/abs/2410.05779

时间

2024年10月8日

作者

北京邮电大学和香港大学

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: this https URL.

检索增强生成（RAG）系统通过整合外部知识源来提升大型语言模型（LLMs），从而生成更准确、更符合用户需求的上下文相关的响应。然而，现有的RAG系统存在显著的局限性，包括依赖于扁平的数据表示以及缺乏足够的上下文意识，这可能导致不完整的答案，无法捕捉复杂的相互依赖关系。为了解决这些挑战，我们提出了LightRAG，该系统将图结构融入文本索引和检索过程中。这一创新框架采用双层检索系统，增强了从低层次和高层次知识发现中全面信息检索的能力。此外，图结构与向量表示的结合促进了相关实体及其关系的有效检索，显著提高了响应速度，同时保持了上下文的相关性。这一能力进一步通过一个增量更新算法得到加强，该算法确保及时整合新数据，使系统在数据环境快速变化的情况下保持有效和响应迅速。广泛的实验验证表明，与现有方法相比，LightRAG在检索准确性和效率方面有显著改进。我们已经将LightRAG开源并提供以下链接：此URL。

Retrieval-Augmented Generation (RAG) systems have been developed to enhance large language models (LLMs) by integrating external knowledge sources Sudhi et al. (2024); Es et al. (2024); Salemi & Zamani (2024). This innovative integration allows LLMs to generate more accurate and contextually relevant responses, significantly improving their utility in real-world applications. By adapting to specific domain knowledge Tu et al. (2024), RAG systems ensure that the information provided is not only pertinent but also tailored to the user’s needs. Furthermore, they offer access to up-to-date information Zhao et al. (2024), which is crucial in rapidly evolving fields. Chunking plays a vital role in facilitating the retrieval-augmented generation process Lyu et al. (2024). By breaking down a large external text corpus into smaller, more manageable segments, chunking significantly enhances the accuracy of information retrieval. This approach allows for more targeted similarity searches, ensuring that the retrieved content is directly relevant to user queries.

检索增强生成（RAG）系统旨在通过整合外部知识源来增强大型语言模型（LLMs），Sudhi等人（2024年）、Es等人（2024年）、Salemi和Zamani（2024年）。这种创新的整合使LLMs能够生成更准确且上下文相关的响应，大大提升了其在实际应用中的实用性。通过适应特定领域的知识Tu等人（2024年），RAG系统确保提供的信息不仅相关，而且针对用户的需求进行了定制。此外，它们提供了访问最新信息的能力Zhao等人（2024年），这对于快速发展的领域至关重要。切块在促进检索增强生成过程中起着关键作用Lyu等人（2024年）。通过将大型外部文本语料库分解成更小、更易管理的部分，切块显著提高了信息检索的准确性。这种方法允许进行更精确的相似性搜索，确保检索到的内容直接与用户查询相关。

However, existing RAG systems have key limitations that hinder their performance. First, many methods rely on flat data representations, restricting their ability to understand and retrieve information based on intricate relationships between entities. Second, these systems often lack the contextual awareness needed to maintain coherence across various entities and their interrelations, resulting in responses that may not fully address user queries. For example, consider a user asking, “How does the rise of electric vehicles influence urban air quality and public transportation infrastructure?” Existing RAG methods might retrieve separate documents on electric vehicles, air pollution, and public transportation challenges but struggle to synthesize this information into a cohesive response. They may fail to explain how the adoption of electric vehicles can improve air quality, which in turn could affect public transportation planning. As a result, the user may receive a fragmented answer that does not adequately capture the complex inter-dependencies among these topics.

然而，现有的RAG系统存在影响性能的关键限制。首先，许多方法依赖于扁平的数据表示形式，限制了它们基于实体之间复杂关系理解和检索信息的能力。其次，这些系统往往缺乏维持各种实体及其相互关系连贯性的上下文意识，导致响应可能无法完全回答用户的问题。例如，考虑一个用户询问：“电动汽车的兴起如何影响城市空气质量及公共交通基础设施？”现有的RAG方法可能会分别检索关于电动汽车、空气污染和公共交通挑战的文档，但在将这些信息综合成一个连贯的响应方面却力不从心。它们可能无法解释电动汽车的采用如何改善空气质量，进而影响公共交通规划。因此，用户可能会收到一个碎片化的答案，无法充分捕捉这些主题之间的复杂相互依赖关系。

To address these limitations, we propose incorporating graph structures into text indexing and relevant information retrieval. Graphs are particularly effective at representing the interdependencies among different entities Rampášek et al. (2022), which enables a more nuanced understanding of relationships. The integration of graph-based knowledge structures facilitates the synthesis of information from multiple sources into coherent and contextually rich responses. Despite these advantages, developing a fast and scalable graph-empowered RAG system that efficiently handles varying query volumes is crucial. In this work, we achieve an effective and efficient RAG system by addressing three key challenges: i) Comprehensive Information Retrieval. Ensuring comprehensive information retrieval that captures the full context of inter-dependent entities from all documents; ii)Enhanced Retrieval Efficiency. Improving retrieval efficiency over the graph-based knowledge structures to significantly reduce response times; iii) Rapid Adaptation to New Data. Enabling quick adaptation to new data updates, ensuring the system remains relevant in dynamic environments.

为了克服这些限制，我们建议将图结构纳入文本索引和相关信息检索中。图特别擅长表示不同实体之间的相互依赖关系Rampášek等人（2022年），这使得对关系的理解更加细致入微。基于图的知识结构的整合有助于从多个来源合成信息，形成连贯且上下文丰富的响应。尽管如此，开发一个快速且可扩展的基于图的RAG系统，以高效处理不同的查询量是至关重要的。在这项工作中，我们通过解决三个关键挑战来实现一个有效且高效的RAG系统：i) 全面的信息检索。确保从所有文档中全面检索信息，捕捉相互依赖实体的完整上下文；ii) 提升检索效率。提高基于图的知识结构上的检索效率，大幅减少响应时间；iii) 快速适应新数据。实现对新数据更新的快速适应，确保系统在动态环境中保持相关性。

In response to the outlined challenges, we propose LightRAG, a model that seamlessly integrates a graph-based text indexing paradigm with a dual-level retrieval framework. This innovative approach enhances the system’s capacity to capture complex inter-dependencies among entities, resulting in more coherent and contextually rich responses. LightRAG employs efficient dual-level retrieval strategies: low-level retrieval, which focuses on precise information about specific entities and their relationships, and high-level retrieval, which encompasses broader topics and themes. By combining both detailed and conceptual retrieval, LightRAG effectively accommodates a diverse range of quries, ensuring that users receive relevant and comprehensive responses tailored to their specific needs. Additionally, by integrating graph structures with vector representations, our framework facilitates efficient retrieval of related entities and relations while enhancing the comprehensiveness of results through relevant structural information from the constructed knowledge graph.

针对上述挑战，我们提出LightRAG，这是一种无缝集成了基于图的文本索引范式与双层检索框架的模型。这一创新方法增强了系统捕捉实体之间复杂相互依赖关系的能力，从而产生更加连贯且上下文丰富的响应。LightRAG采用了高效的双层检索策略：低层检索专注于特定实体及其关系的精确信息，而高层检索则涵盖更广泛的主题和主题。通过结合详细和概念检索，LightRAG有效地满足了多样化的查询需求，确保用户获得与其具体需求相匹配的相关且全面的响应。此外，通过将图结构与向量表示相结合，我们的框架促进了相关实体和关系的有效检索，同时通过构建的知识图中的相关结构信息增强了结果的全面性。

In summary, the key contributions of this work are highlighted as follows:

General Aspect. We emphasize the importance of developing a graph-empowered RAG system to overcome the limitations of existing methods. By integrating graph structures into text indexing, we can effectively represent complex interdependencies among entities, fostering a nuanced understanding of relationships and enabling coherent, contextually rich responses.

Methodologies. To enable an efficient and adaptive RAG system, we propose LightRAG, which integrates a dual-level retrieval paradigm with graph-enhanced text indexing. This approach captures both low-level and high-level information for comprehensive, cost-effective retrieval. By eliminating the need to rebuild the entire index, LightRAG reduces computational costs and accelerates adaptation, while its incremental update algorithm ensures timely integration of new data, maintaining effectiveness in dynamic environments.

Experimental Findings. Extensive experiments were conducted to evaluate the effectiveness of LightRAG in comparison to existing RAG models. These assessments focused on several key dimensions, including retrieval accuracy, model ablation, response efficiency, and adaptability to new information. The results demonstrated significant improvements over baseline methods.

总之，本研究的主要贡献如下：

一般方面。我们强调开发基于图的RAG系统的重要性，以克服现有方法的局限性。通过将图结构整合到文本索引中，我们可以有效地表示实体之间的复杂相互依赖关系，促进对关系的细致理解，并实现连贯、上下文丰富的响应。

方法。为了实现一个高效且适应性强的RAG系统，我们提出了LightRAG，它将双层检索范式与基于图增强的文本索引相结合。这种方法捕获了低层次和高层次的信息，实现了全面且成本效益高的检索。通过消除重建整个索引的需要，LightRAG减少了计算成本并加速了适应过程，其增量更新算法确保了新数据的及时整合，保持了系统在动态环境中的有效性。

实验结果。进行了广泛的实验以评估LightRAG相对于现有RAG模型的有效性。这些评估集中在几个关键维度上，包括检索准确性、模型消融、响应效率以及对新信息的适应性。结果表明，与基线方法相比，LightRAG有了显著的改进。

This work introduces an advancement in Retrieval-Augmented Generation (RAG) through the integration of a graph-based indexing approach that enhances both efficiency and comprehension in information retrieval. LightRAG utilizes a comprehensive knowledge graph to facilitate rapid and relevant document retrieval, enabling a deeper understanding of complex queries. Its dual-level retrieval paradigm allows for the extraction of both specific and abstract information, catering to diverse user needs. Furthermore, LightRAG’s seamless incremental update capability ensures that the system remains current and responsive to new information, thereby maintaining its effectiveness over time. Overall, LightRAG excels in both efficiency and effectiveness, significantly improving the speed and quality of information retrieval and generation while reducing costs for LLM inference.

本文介绍了一种名为LightRAG的改进型Retrieval-Augmented Generation（RAG）方法，通过采用基于图的索引方法，提高了信息检索的效率和理解能力。LightRAG利用全面的知识图谱，加快了相关文档的检索速度，使复杂查询的理解更加深入。其双层检索范式既能提取具体的信息，又能提取抽象的信息，以满足用户多样化的需求。此外，LightRAG无缝的增量更新能力确保系统能够实时更新并响应新信息，从而在长期内保持其有效性。总的来说，LightRAG在效率和效果方面表现卓越，显著提高了信息检索和生成的速度和质量，同时降低了LLM推理的成本。