Retrieval Augmented Generation Approach for Multipdf Chatbot using LangChain

This paper presents an AI-driven application designed to facilitate information retrieval and conversational interactions through uploaded PDF documents. In an era where a vast amount of information is contained within PDFs, it is essential to have efficient extraction and interaction mechanisms. The purpose of this paper is to bridge the gap between users and the wealth of knowledge contained in these documents. The research problem is addressed by utilizing advanced AI technologies to streamline document processing and enhance user engagement. The paper emphasizes the importance of efficient information retrieval and user-friendly interfaces in addressing existing challenges in accessing and discussing content within PDF documents. Methodologically, the paper integrates various technologies, including Google’s Generative AI and FAISS, to enable seamless document processing, text segmentation, and embedding generation. Key findings underscore the effectiveness of the implemented methodologies in facilitating interactive communication and extracting relevant information from PDFs. The paper’s conclusions highlight the it’s utility as a versatile tool for knowledge sharing and information retrieval. Limitations are acknowledged, emphasizing the need for ongoing research and improvement in addressing emerging challenges. Overall, this research contributes to advancing AI-driven applications for document processing and interactive communication, paving the way for enhanced knowledge accessibility and user engagement in diverse domains.