CHAT WITH YOUR PDF - MULTI DOCUMENT Q&A WITH SOURCE CITATIONS
Abstract
The Chat with Your PDF — Multi-Document Q&A with Source Citations system is an intelligent document-question-answering platform that allows users to interact with multiple PDF documents using natural language. Users often need to read large documents such as research papers, textbooks, reports, manuals, policies, and business documents to find specific information. Searching through multiple PDFs manually can be time-consuming and inefficient. The proposed system uses Natural Language Processing, document processing, embeddings, vector databases, Retrieval-Augmented Generation (RAG), and Generative AI to understand user questions and retrieve relevant information from uploaded PDF documents. The system extracts text from documents, divides it into meaningful chunks, generates vector embeddings, and stores them in a searchable vector database. When a user submits a question, the system converts the question into an embedding and searches across the indexed documents to identify the most relevant content. The retrieved document sections are then provided as context to a Generative AI model, which generates a natural-language answer based on the retrieved information. This approach helps reduce the dependence on manually reading entire documents. A major feature of the proposed system is source citation. Along with the generated answer, the system displays references to the document, page number, section, or relevant text segment used to formulate the response. This improves transparency and allows users to verify the generated information against the original PDF documents. Overall, the system provides a convenient and interactive way to explore multiple documents through a single conversational interface. It can be useful for students, researchers, employees, legal teams, analysts, and organizations that frequently work with large collections of PDF files. Future enhancements can include multilingual document support, table and image understanding, OCR for scanned PDFs, multimodal RAG, document comparison, advanced citation verification, and enterprise access control.
