SAKR: Enhancing Retrieval-Augmented Generation via Streaming Algorithm and K-Means Clustering (2407.21300v4)

Abstract: Retrieval-augmented generation (RAG) has achieved significant success in information retrieval to assist LLMs LLMs because it builds an external knowledge database. However, it also has many problems, it consumes a lot of memory because of the enormous database, and it cannot update the established index database in time when confronted with massive streaming data. To reduce the memory required for building the database and maintain accuracy simultaneously, we proposed a new approach integrating a streaming algorithm with k-means clustering into RAG. Our approach applied a streaming algorithm to update the index dynamically and reduce memory consumption. Additionally, the k-means algorithm clusters highly similar documents, and the query time would be shortened. We conducted comparative experiments on four methods, and the results indicated that RAG with streaming algorithm and k-means clusters outperforms traditional RAG in accuracy and memory, particularly when dealing with large-scale streaming data.

• The paper demonstrates that integrating a heavy hitters streaming algorithm with k-means clustering significantly reduces memory usage in RAG by up to 90%.
• It employs dynamic index updating and clustering to optimize query retrieval efficiency while maintaining high accuracy.
• Experimental results on a dataset of over 16,000 news articles validate the model’s effectiveness for real-time, large-scale applications.

An Enhanced Retrieval-Augmented Generation Model with Streaming Algorithms and k-Means Clustering

This paper, authored by Haoyu Kang, Yukun Zhong, Yuzhou Zhu, and Ke Wang, presents a methodological advancement in the field of Retrieval-Augmented Generation (RAG) for LLMs. The authors primary aim is to tackle two main inefficiencies inherent in traditional RAG models: excessive memory consumption and the inability to update index databases in real time when dealing with voluminous streaming data.

Introduction to RAG and Its Limitations

The conventional RAG model provides a promising framework for improving the performance of LLMs on knowledge-intensive tasks by leveraging an external knowledge database. This model processes input queries by embedding them into vectors, retrieving the most similar documents stored in an index, and generating enhanced responses with the combined information. However, the burgeoning volume of data introduces significant challenges:

1. Memory Consumption: Storing an extensive index database demands substantial memory.
2. Temporal Relevance: The static nature of traditional RAG's database means that it struggles to maintain accuracy with frequently updated data.

Proposed Method: Streaming Algorithm and k-Means Clustering in RAG

To ameliorate these issues, the authors propose the integration of a streaming algorithm with the k-means clustering algorithm within the RAG framework.

Heavy Hitters Streaming Algorithm

The heavy hitters streaming algorithm is employed to prioritize memory efficiency. It continuously updates the index database by retaining only the most relevant documents as identified by the algorithm. This process entails:

• Embedding Queries and Data: Queries and input data are embedded into vectors.
• Database Structuring: A data structure that consumes merely 10% of the memory compared to Naive RAG is initialized.
• Dynamic Updating: The algorithm iteratively updates this structure by replacing the least relevant documents with those exhibiting higher similarity scores to the query.

k-Means Clustering

Following the initial filtering via the streaming algorithm, the k-means clustering technique is applied to enhance query retrieval efficiency. By clustering documents with high similarity:

• Optimized Query Time: The method allows retrieving from a limited number of clusters rather than the entire database, significantly reducing computational time.
• Maintenance of Accuracy: This dual approach ensures that the clustering does not compromise retrieval accuracy.

Experimental Results

The experiments utilized a dataset comprising more than sixteen thousand news articles and comments with respective annotation to assess the model's performance.

Comparative Evaluation

Accuracy and memory consumption were evaluated across four models: Naive RAG, RAG with a streaming algorithm, RAG with k-means clustering, and RAG with both streaming and k-means clustering. The findings indicated:

• Memory Efficiency: The combined method achieved a 90% reduction in memory usage compared to Naive RAG.
• Accuracy: The hybrid model preserved higher accuracy levels even under high query loads.

Clustering Effectiveness

Further evaluations demonstrated that the streaming algorithm significantly improved the clustering quality as compared to using the k-means clustering algorithm in isolation.

Implications and Future Directions

The integration of streaming algorithms with k-means clustering in RAG models addresses critical challenges by balancing memory usage and retrieval accuracy. The technology potentially can be extended to other RAG variations, such as Microsoft's Graph RAG, especially for tasks involving lengthy texts and more intricate knowledge graphs.

For future investigations, the exploration of advanced clustering techniques like kernel k-means could offer improvements in handling complex data structures. Additionally, employing these methodologies could facilitate more efficient and accurate LLM applications in real-time, evolving data environments.

Conclusion

This paper makes substantial contributions to enhancing the RAG framework, specifically for environments with streaming data. The proposed integration not only optimizes resource utilization but also maintains high retrieval efficacy, paving the way for further advancements in AI and information retrieval systems.

The paper references seminal works and recent advancements, including the introduction of RAG by Lewis et al., various streaming algorithms, and clustering methods, offering a comprehensive overview of the employed techniques.

In conclusion, the proposed combination of a transformed heavy hitters algorithm with k-means clustering into the RAG framework significantly improves its performance in terms of memory efficiency and accuracy, making it well-suited for managing large-scale streaming data scenarios.