A Large Language Model based Web Application for Contextual Document Conversation: A Large Language Model based Web Application for Contextual Document Conversation

Asad Khan; Dr. Abdul Haseeb Malik; Talha Ahsan; Qazi Ejaz Ali

Authors

Asad khan University of Peshawar, Pakistan
Dr. Abdul Haseeb Malik University of Peshawar, Pakistan
Talha Ahsan University of Peshawar, Pakistan
Dr Qazi Ejaz Ali University of Peshawar, Pakistan

Keywords:

Application Learning, Natural Language Processing, AI Bots, Large Learning model, Contextual Document Conversation

Abstract

The emergence of LLMs, such as ChatGPT, Gemini, and Claude has ushered in a new era of natural language processing, enabling rich textual interactions with computers. However, despite the capabilities of these new language models, they face significant challenges when queried on recent information or private data not included in the model’s dataset. Retrieval Augmented Generation (RAG) overcame the problems mentioned earlier by augmenting user queries with relevant context from a user-provided document(s), thus grounding the model’s response to inaccurate source material. In research, RAG enables users to engage interactively with their documents, instead of manually reading through their document(s). Users provide their document(s) to the system, which is then converted into vector indices, and used to inject contextual information into the user prompt during retrieval. The augmented prompt then enables the language model to contextually answer user queries. The research is composed of a web application, with an intuitive interface for interacting with the LIama 3.2 1B, an open-source LLM. Users can upload their document(s) and chat with the LLM in the context of their uploaded document(s).

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