Scholarlink

Conversational AI technologies have transformed human- machine interactions in educational settings, such as the SRM Institute of Science and Technology, where intelligent chatbots play pivotal roles in supporting students, faculty, and staff. This research presents a sophisticated chatbot system tailored for the SRM Institute, leveraging cutting-edge technologies like the LangChain framework and Large Language Models (LLMs), including OpenAI's GPT-3.5 Turbo, to enhance language understanding and generation capabilities. The methodology emphasizes meticulous data preparation, including data collection, preprocessing, and embedding creation, integrated with vector databases to establish a robust knowledge base. The chatbot's user interface, built with Flask, ensures an intuitive user experience with a visually appealing and responsive layout. Evaluation results demonstrate a 95% accuracy rate and high reliability in handling diverse user inquiries, highlighting the chatbot's capacity to manage growing volumes of queries while maintaining consistent accuracy. These outcomes underscore significant improvements in user engagement and satisfaction, marking a substantial advancement in educational chatbot technology.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Leveraging LangChain Framework and Large Language Models for Conversational Chatbot Development

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • R. Ashish Tarun,
  • B. Priyadarshini,
  • M. Sneha,
  • K. Akila

摘要

Conversational AI technologies have transformed human- machine interactions in educational settings, such as the SRM Institute of Science and Technology, where intelligent chatbots play pivotal roles in supporting students, faculty, and staff. This research presents a sophisticated chatbot system tailored for the SRM Institute, leveraging cutting-edge technologies like the LangChain framework and Large Language Models (LLMs), including OpenAI's GPT-3.5 Turbo, to enhance language understanding and generation capabilities. The methodology emphasizes meticulous data preparation, including data collection, preprocessing, and embedding creation, integrated with vector databases to establish a robust knowledge base. The chatbot's user interface, built with Flask, ensures an intuitive user experience with a visually appealing and responsive layout. Evaluation results demonstrate a 95% accuracy rate and high reliability in handling diverse user inquiries, highlighting the chatbot's capacity to manage growing volumes of queries while maintaining consistent accuracy. These outcomes underscore significant improvements in user engagement and satisfaction, marking a substantial advancement in educational chatbot technology.