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

Computers, Materials & Continua 2024年第8期80卷 2423-2442页

作者： S.Vidivelli Manikandan Ramachandran A.Dharunbalaji School of Computing SASTRA Deemed UniversityThanjavurTamilnadu613401India

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.

关键词： LangChain retrieval augumental generation(RAG) fine tuning

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