Development of a ChatBot model for health telecare: Integration of LangChain, embeddings with OpenAI, and Pinecone using the question answering technique

Abstract

This study was dedicated to the development of an advanced model for a medical ChatBot, aimed at optimizing Telecare in healthcare. Cutting-edge technologies such as LangChain, Embeddings with OpenAI, and Pinecone were incorporated, applying Question Answering techniques to enhance user-Bot interaction. Google Colab was essential, serving as the main environment for both running the code and the ChatBot, in addition to facilitating intensive data processing. The methodology involved a manual and rigorous data collection from official medical sources to ensure the accuracy and reliability of the information integrated into the ChatBot. The results showed a notable variability in response times between GPT-3.5-turbo and GPT-4, with a comprehensive cost evaluation to analyze the profitability of both models in specific medical contexts. In conclusion, the study provided significant insights into the implementation and efficacy of advanced medical ChatBot models, revealing crucial considerations about optimizing response times and cost evaluations in the development of innovative Telecare solutions in healthcare.