Abstract: In the vanguard of knowledge retrieval,particularly in scenarios involving large language models(LLMs),research emphasis has shifted toward employing pure vector retrieval techniques for efficient capture of pertinent information.This information is then fed into large language models for comprehensive distillation and summarization.However,the limitations of this approach lie in its potential inability to fully encompass the intricacies of retrieval through vector representations alone,coupling with an absence of effective ranking mechanisms.This often leads to an overabundance of irrelevant information,thereby diluting the alignment between the final response and the user's actual needs.To address this conundrum,this paper introduces a hybrid retrieval-augmented dual-tower model.This model innovatively integrates a multi-path recall strategy,ensuring that the retrieval results are both comprehensive and highly relevant through complementary recall mechanisms.Architecturally,it adopts a dual-la-yer structure,combining bidirectional recurrent neural networks with text convolutional neural networks.This allows the model to perform multi-level ranking optimization on retrieval results,significantly enhancing the relevance and the precision of top-ranking outcomes.Moreover,the high-quality information,efficiently ranked,is integrated with the original query and fed into a large language model.This exploits the model's deep analytical capabilities to generate more accurate and credible responses.Experimental findings affirm that the proposed method effectively improves retrieval accuracy and system performance overall,markedly enhancing the precision and practicality of large language models in real-world applications.

Key words: Knowledge search, Large language models, Vector retrieval technology, Hybrid retrieval-augmented dual-tower model, Multi-path recall strategy