An End-to-End Framework for Multi-Docs Chatbot using Llama2

The evolution of conversational agents, in particular the case with chatbots, has experienced huge boosts in recent years, enabling a variety of tasks and allowing users to enjoy much more interaction. This research presents a sequential model for a Chatbot of multiple documents that is based on the best of the Llama2 mod-el. The document classification framework intends to offer a user-oriented as well as a versatile conversational approach that draws on data from several fields. Through proper implementation of state-of-the-art natural language processing technology, the chatbot can understand users' inquiries, retrieve the required in-formation from the uploaded files, and respond fluently and understandably. It provides document management processes, like file handling of PDF, DOCX, etc., which enables the user to work with almost all file types and formats. And that directly uses Hugging Face Transformers in such processes as text embed-ding and conversational generation. One of the key components of the system is the FAISS tool that allows for vector storage and retrieval keeping the chatbot operating efficiently in the process of searching and retrieving information from vast document collections. In summary, the work provided here lays out the foundations of the multi-doc system which is a powerful tool that can be used to improve the deployments and information search tasks, with the effect of boost-ing user engagement and productivity.