Abstract: In the dynamic field of web development, the integration of sophisticated AI technologies for query processing has become increasingly crucial. This paper presents a framework that significantly improves the relevance of web query responses by leveraging cutting-edge technologies like Hugging Face, FAISS, Google PaLM, Gemini, and LangChain. We explore and compare the performance of both PaLM and Gemini, two powerful LLMs, to identify strengths and weaknesses in the context of web development query retrieval. Our approach capitalizes on the synergistic combination of these freely accessible tools, ultimately leading to a more efficient and user-friendly query processing system.

Xiaoli Huan and Hong Zhou. “Integrating Advanced Language Models and Vector Database for Enhanced AI Query Retrieval in Web Development”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.6 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150601

@article{Huan2024,
title = {Integrating Advanced Language Models and Vector Database for Enhanced AI Query Retrieval in Web Development},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150601},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150601},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {6},
author = {Xiaoli Huan and Hong Zhou}
}