Advancing Retrieval-Augmented Generation for Enhanced QA Performance: A Multi-Agent ChatPDF Approach

PDF documents often contain critical information, yet navigating through them manually to extract useful insights can be slow and inefficient.This study introduces ChatPDF-a conversational system designed to simplify the process of querying lengthy PDF files.By combining recent advancements in information retrieval with natural language generation techniques, the system allows users to ask questions in plain language and receive accurate, document-based answers.ChatPDF uses a layered, task-specific approach where different components handle document reading, content matching, and response formulation independently.Tested on a range of real-world documents, the system consistently provided fast, relevant, and user-friendly results.The proposed approach demonstrates how blending retrieval techniques with structured agent workflows can improve how users interact with large volumes of digital text.