## 5 Conclusions  
This study designed and implemented a generative LLM utilizing a local knowledge base with BM-RAGAM hybrid retrieval technology, enhanced for marine NLP tasks. This approach combined both RAG and BM25 retrieval methods to improve the process, making it more relevant and accurate in the context of marine-related data and tasks. In this study, the Qwen2-72B model was used as the base, was combined with the LangChain framework, and achieved a connection with the external knowledge base by integrating OpenAI API. Through this integration, this model can dynamically retrieve relevant external knowledge while generating text, which not only expands the scope of the model's knowledge, but also effectively reduces the risk of hallucinations in LLMs. At the same time, this solution significantly enhances the interpretability of model generation results, ensuring that the generated content is closely related to external facts, especially for tasks involving the marine field that require accurate knowledge support. Ultimately, this combined retrieval and generation model can provide richer, more reliable, and interpretive text output when dealing with complex problems.  
While the BM-RAGAM scheme demonstrates superior relevance and domain-specific accuracy, its less-segmented output format may hinder comprehension for certain users. Future work will focus on enhancing the text structuring mechanisms, such as introducing hierarchical generation strategies or explicit segmentation controls, to improve the clarity and accessibility of the responses without compromising their accuracy.  
At the same time, the vigorous development of multimodal technology, especially the introduction of non-text input forms such as speech and images, is pushing vector databases to develop in a more diversified direction. Future knowledge base systems will not only be limited to processing text but will also be extended to handling more complex multimodal data, which will greatly expand their application range and flexibility. With the continuous iteration of large model and vector database technology, the knowledge base system will continue to evolve in the aspects of multimodal input, diversified data processing, and efficient querying, and provide users with more abundant and comprehensive knowledge acquisition channels. This trend will not only have a profound impact on scientific research and industry, but will also further promote the process of information and intelligence in various industries.  
Conceptualization, W.Z.; methodology, W.Z., Q.C., J.Y. and J.C.; software and validation, Q.C.; writing--original draft preparation, Q.C.; writing--review and revising, W.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was financially supported by the National Key R&D Program of China (No. 2023 YFD2401303) and the Central Public-Interest Scientific Institution Basal Research Fund, ECSFR, CAFS (No. 2022 ZD0402).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.