Implementing Streaming algorithm and k-means clusters to RAG
<|reference_start|>Implementing Streaming algorithm and k-means clusters to RAG: Retrieval-augmented generation (RAG) has achieved significant success in information retrieval to assist large language models 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.<|reference_end|>
@article{kang2024implementing,
    title={Implementing Streaming algorithm and k-means clusters to RAG},
    author={Haoyu Kang (1), Yuzhou Zhu (2), Yukun Zhong (3), Ke Wang (4) ((1)
  Central South University, (2) Dalian University of Technology, (3) Nanjing
  University, (4) Xidian University)},
    journal={arXiv preprint arXiv:2407.21300},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.21300},
    primaryClass={cs.IR cs.AI}
}