Efficiency-Driven Custom Chatbot Development: Unleashing LangChain, RAG, and Performance-Optimized LLM Fusion

作     者：S.Vidivelli Manikandan Ramachandran A.Dharunbalaji 

作者机构：School of ComputingSASTRA Deemed UniversityThanjavurTamilnadu613401India 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2024年第80卷第8期

页      面：2423-2442页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：LangChain retrieval augumental generation(RAG) fine tuning 

摘      要：This exploration acquaints a momentous methodology with custom chatbot improvement that focuses on pro-ficiency close by *** accomplish this by joining three key innovations:LangChain,Retrieval Augmented Generation(RAG),and enormous language models(LLMs)tweaked with execution proficient strategies like LoRA and *** takes into consideration fastidious fitting of chatbots to explicit purposes,guaranteeing engaged and important collaborations with ***’s web scratching capacities engage these chatbots to get to a tremendous store of data,empowering them to give exhaustive and enlightening reactions to *** recovered data is then decisively woven into reaction age utilizing LLMs that have been calibrated with an emphasis on execution *** combination approach offers a triple advantage:further developed viability,upgraded client experience,and extended admittance to *** become proficient at taking care of client questions precisely and productively,while instructive and logically pertinent reactions make a more regular and drawing in cooperation for *** last,web scratching enables chatbots to address a more extensive assortment of requests by conceding them admittance to a more extensive information *** digging into the complexities of execution proficient LLM calibrating and underlining the basic job of web-scratched information,this examination offers a critical commitment to propelling custom chatbot plan and *** subsequent chatbots feature the monstrous capability of these advancements in making enlightening,easy to understand,and effective conversational specialists,eventually changing the manner in which clients cooperate with chatbots.