Publication Date: 2023/11/20
Abstract: This research paper introduces a cutting-edge healthcare chatbot that harnesses the synergy of Llama2, Faiss, and Hugging Face embeddings to optimize responses to intricate medical inquiries. Leveraging a meticulously curated training corpus of medical literature, this chatbot significantly augments its semantic understanding and responsiveness. The integration of Llama2 bolsters the chatbot’s contextual comprehension, while Faiss enables expedited, similarity-based information retrieval from an extensive library of medical texts. Hugging Face embed-dings facilitate contextually coherent response generation. The results affirm substantial enhancements in the chatbot’s efficacy in delivering technically informed and contextually precise medical responses. This promising innovation offers a powerful tool for disseminating validated medical knowledge, serving as an invaluable resource for healthcare professionals and patients alike.
Keywords: Healthcare Chatbot, Llama2, Faiss, Hugging Face Embeddings.
DOI: https://doi.org/10.5281/zenodo.10159700
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23NOV666.pdf
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