Date
Publisher
arXiv
This article introduces Bio-Eng-LMM AI chatbot, a versatile platform designed
to enhance user interaction for educational and research purposes. Leveraging
cutting-edge open-source Large Language Models (LLMs), Bio-Eng-LMM operates as
a sophisticated AI assistant, exploiting the capabilities of traditional models
like ChatGPT. Central to Bio-Eng-LMM is its implementation of Retrieval
Augmented Generation (RAG) through three primary methods: integration of
preprocessed documents, real-time processing of user-uploaded files, and
information retrieval from any specified website. Additionally, the chatbot
incorporates image generation via a Stable Diffusion Model (SDM), image
understanding and response generation through LLAVA, and search functionality
on the internet powered by secure search engine such as DuckDuckGo. To provide
comprehensive support, Bio-Eng-LMM offers text summarization, website content
summarization, and both text and voice interaction. The chatbot maintains
session memory to ensure contextually relevant and coherent responses. This
integrated platform builds upon the strengths of RAG-GPT and Web-Based RAG
Query (WBRQ) where the system fetches relevant information directly from the
web to enhance the LLMs response generation.
What is the application?
Who age?
Why use AI?
Study design
